Postprandial Glucose Regulation Via KNN Meal Classification in Type 1 Diabetes

Abstract

Blood glucose concentration control is a classic negative feedback problem with insulin secreted by the pancreas as a control variable. Type 1 Diabetes is a chronic metabolic disease caused by a cellular-mediated autoimmune destruction of the pancreas beta-cells, so exogenous insulin administration is needed to regulate the glycaemia. Postprandial glucose regulation is typically based on the knowledge of an estimation of the ingested carbohydrates, of the Carbohydrate-to-insulin ratio, of the correction factor, of the insulin still active and of a measure of the glycaemia just before the meal. Despite the use of this information meal compensation is yet a key unsolved issue. In this paper a new approach based on machine-learning methodologies is proposed to improve postprandial glucose regulation. The proposed approach uses the multiple K-Nearest Neighbors classification algorithm to predict postprandial glucose profile due to the nominal therapy and to suggest a correction to time and/or amount of the meal bolus. This approach has been successfully validated on the adult in silico population of the UVA/PADOVA simulator, which has been accepted by the Food and Drug Administration as a substitute to animal trials.